<<<<<<< Updated upstream Pandas Profiling Report

Overview

Dataset statistics

Number of variables28
Number of observations8674
Missing cells8659
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries14
Numeric2

Alerts

State Code has constant value "4" Constant
County Code has constant value "13" Constant
Site Num has constant value "3003" Constant
Address has constant value "2857 N MILLER RD-S SCOTTSDALE STN" Constant
State has constant value "Arizona" Constant
County has constant value "Maricopa" Constant
City has constant value "Scottsdale" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
CO 1st Max Value is highly correlated with NO2 AQI and 2 other fieldsHigh correlation
CO 1st Max Hour is highly correlated with NO2 1st Max HourHigh correlation
State Code is highly correlated with Site Num and 9 other fieldsHigh correlation
County Code is highly correlated with State Code and 9 other fieldsHigh correlation
Site Num is highly correlated with State Code and 9 other fieldsHigh correlation
Address is highly correlated with State Code and 9 other fieldsHigh correlation
State is highly correlated with State Code and 9 other fieldsHigh correlation
County is highly correlated with State Code and 9 other fieldsHigh correlation
City is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
O3 Units is highly correlated with State Code and 9 other fieldsHigh correlation
SO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
CO Units is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 4 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 1 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 8 other fieldsHigh correlation
O3 Mean is highly correlated with O3 1st Max Value and 1 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with NO2 AQI and 6 other fieldsHigh correlation
SO2 AQI is highly correlated with NO2 AQI and 4 other fieldsHigh correlation
CO Mean is highly correlated with NO2 Mean and 2 other fieldsHigh correlation
CO AQI is highly correlated with NO2 AQI and 4 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 5 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 AQI has 4336 (50.0%) missing values Missing
CO AQI has 4323 (49.8%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
SO2 1st Max Hour is non stationary Non stationary
SO2 AQI is non stationary Non stationary
CO Mean is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
SO2 1st Max Hour is seasonal Seasonal
SO2 AQI is seasonal Seasonal
CO Mean is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 660 (7.6%) zeros Zeros
O3 1st Max Hour has 148 (1.7%) zeros Zeros
SO2 Mean has 218 (2.5%) zeros Zeros
SO2 1st Max Value has 218 (2.5%) zeros Zeros
SO2 1st Max Hour has 941 (10.8%) zeros Zeros
SO2 AQI has 108 (1.2%) zeros Zeros
CO 1st Max Hour has 2160 (24.9%) zeros Zeros

Reproduction

Analysis started2022-10-20 17:52:44.369501
Analysis finished2022-10-20 17:52:57.520945
Duration13.15 seconds
Software versionpandas-profiling v3.3.1
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
4
8674 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8674
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
48674
100.0%

Length

2022-10-20T18:52:57.609791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Overview

Dataset statistics

Number of variables28
Number of observations8674
Missing cells8659
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries14
Numeric2

Alerts

State Code has constant value "4" Constant
County Code has constant value "13" Constant
Site Num has constant value "3003" Constant
Address has constant value "2857 N MILLER RD-S SCOTTSDALE STN" Constant
State has constant value "Arizona" Constant
County has constant value "Maricopa" Constant
City has constant value "Scottsdale" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
CO 1st Max Value is highly correlated with NO2 AQI and 2 other fieldsHigh correlation
CO 1st Max Hour is highly correlated with NO2 1st Max HourHigh correlation
State Code is highly correlated with Address and 9 other fieldsHigh correlation
County Code is highly correlated with Address and 9 other fieldsHigh correlation
Site Num is highly correlated with Address and 9 other fieldsHigh correlation
Address is highly correlated with State and 9 other fieldsHigh correlation
State is highly correlated with Address and 9 other fieldsHigh correlation
County is highly correlated with Address and 9 other fieldsHigh correlation
City is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
O3 Units is highly correlated with Address and 9 other fieldsHigh correlation
SO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
CO Units is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 4 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 1 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 8 other fieldsHigh correlation
O3 Mean is highly correlated with O3 1st Max Value and 1 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with NO2 AQI and 6 other fieldsHigh correlation
SO2 AQI is highly correlated with NO2 AQI and 4 other fieldsHigh correlation
CO Mean is highly correlated with NO2 Mean and 2 other fieldsHigh correlation
CO AQI is highly correlated with NO2 AQI and 4 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 5 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 AQI has 4336 (50.0%) missing values Missing
CO AQI has 4323 (49.8%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
SO2 1st Max Hour is non stationary Non stationary
SO2 AQI is non stationary Non stationary
CO Mean is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
SO2 1st Max Hour is seasonal Seasonal
SO2 AQI is seasonal Seasonal
CO Mean is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 660 (7.6%) zeros Zeros
O3 1st Max Hour has 148 (1.7%) zeros Zeros
SO2 Mean has 218 (2.5%) zeros Zeros
SO2 1st Max Value has 218 (2.5%) zeros Zeros
SO2 1st Max Hour has 941 (10.8%) zeros Zeros
SO2 AQI has 108 (1.2%) zeros Zeros
CO 1st Max Hour has 2160 (24.9%) zeros Zeros

Reproduction

Analysis started2022-10-20 18:30:54.197060
Analysis finished2022-10-20 18:31:01.155964
Duration6.96 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
4
8674 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8674
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
48674
100.0%

Length

2022-10-20T19:31:01.215626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:57.738387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:01.302993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
48674
100.0%

Most occurring characters

ValueCountFrequency (%)
48674
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8674
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
48674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8674
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
48674
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48674
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
13
8674 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters17348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
138674
100.0%

Length

2022-10-20T18:52:57.853999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
48674
100.0%

Most occurring characters

ValueCountFrequency (%)
48674
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8674
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
48674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8674
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
48674
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48674
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
13
8674 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters17348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
138674
100.0%

Length

2022-10-20T19:31:01.362018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:57.987187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:01.434015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
138674
100.0%

Most occurring characters

ValueCountFrequency (%)
18674
50.0%
38674
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
18674
50.0%
38674
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common17348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
18674
50.0%
38674
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18674
50.0%
38674
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
3003
8674 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters34696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3003
2nd row3003
3rd row3003
4th row3003
5th row3003

Common Values

ValueCountFrequency (%)
30038674
100.0%

Length

2022-10-20T18:52:58.098484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
138674
100.0%

Most occurring characters

ValueCountFrequency (%)
18674
50.0%
38674
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
18674
50.0%
38674
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common17348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
18674
50.0%
38674
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18674
50.0%
38674
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
3003
8674 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters34696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3003
2nd row3003
3rd row3003
4th row3003
5th row3003

Common Values

ValueCountFrequency (%)
30038674
100.0%

Length

2022-10-20T19:31:01.500241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:58.239348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:01.572002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
30038674
100.0%

Most occurring characters

ValueCountFrequency (%)
317348
50.0%
017348
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number34696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
317348
50.0%
017348
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common34696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
317348
50.0%
017348
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII34696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
317348
50.0%
017348
50.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
2857 N MILLER RD-S SCOTTSDALE STN
8674 

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters286242
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2857 N MILLER RD-S SCOTTSDALE STN
2nd row2857 N MILLER RD-S SCOTTSDALE STN
3rd row2857 N MILLER RD-S SCOTTSDALE STN
4th row2857 N MILLER RD-S SCOTTSDALE STN
5th row2857 N MILLER RD-S SCOTTSDALE STN

Common Values

ValueCountFrequency (%)
2857 N MILLER RD-S SCOTTSDALE STN8674
100.0%

Length

2022-10-20T18:52:58.353609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
30038674
100.0%

Most occurring characters

ValueCountFrequency (%)
317348
50.0%
017348
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number34696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
317348
50.0%
017348
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common34696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
317348
50.0%
017348
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII34696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
317348
50.0%
017348
50.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
2857 N MILLER RD-S SCOTTSDALE STN
8674 

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters286242
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2857 N MILLER RD-S SCOTTSDALE STN
2nd row2857 N MILLER RD-S SCOTTSDALE STN
3rd row2857 N MILLER RD-S SCOTTSDALE STN
4th row2857 N MILLER RD-S SCOTTSDALE STN
5th row2857 N MILLER RD-S SCOTTSDALE STN

Common Values

ValueCountFrequency (%)
2857 N MILLER RD-S SCOTTSDALE STN8674
100.0%

Length

2022-10-20T19:31:01.635652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:58.491176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:01.711258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
28578674
16.7%
n8674
16.7%
miller8674
16.7%
rd-s8674
16.7%
scottsdale8674
16.7%
stn8674
16.7%

Most occurring characters

ValueCountFrequency (%)
43370
15.2%
S34696
12.1%
T26022
 
9.1%
L26022
 
9.1%
E17348
 
6.1%
D17348
 
6.1%
N17348
 
6.1%
R17348
 
6.1%
O8674
 
3.0%
C8674
 
3.0%
Other values (8)69392
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter199502
69.7%
Space Separator43370
 
15.2%
Decimal Number34696
 
12.1%
Dash Punctuation8674
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S34696
17.4%
T26022
13.0%
L26022
13.0%
E17348
8.7%
D17348
8.7%
N17348
8.7%
R17348
8.7%
O8674
 
4.3%
C8674
 
4.3%
I8674
 
4.3%
Other values (2)17348
8.7%
Decimal Number
ValueCountFrequency (%)
28674
25.0%
88674
25.0%
78674
25.0%
58674
25.0%
Space Separator
ValueCountFrequency (%)
43370
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin199502
69.7%
Common86740
30.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S34696
17.4%
T26022
13.0%
L26022
13.0%
E17348
8.7%
D17348
8.7%
N17348
8.7%
R17348
8.7%
O8674
 
4.3%
C8674
 
4.3%
I8674
 
4.3%
Other values (2)17348
8.7%
Common
ValueCountFrequency (%)
43370
50.0%
-8674
 
10.0%
28674
 
10.0%
88674
 
10.0%
78674
 
10.0%
58674
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII286242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43370
15.2%
S34696
12.1%
T26022
 
9.1%
L26022
 
9.1%
E17348
 
6.1%
D17348
 
6.1%
N17348
 
6.1%
R17348
 
6.1%
O8674
 
3.0%
C8674
 
3.0%
Other values (8)69392
24.2%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Arizona
8674 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters60718
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona8674
100.0%

Length

2022-10-20T18:52:58.608198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
28578674
16.7%
n8674
16.7%
miller8674
16.7%
rd-s8674
16.7%
scottsdale8674
16.7%
stn8674
16.7%

Most occurring characters

ValueCountFrequency (%)
43370
15.2%
S34696
12.1%
T26022
 
9.1%
L26022
 
9.1%
E17348
 
6.1%
D17348
 
6.1%
N17348
 
6.1%
R17348
 
6.1%
O8674
 
3.0%
C8674
 
3.0%
Other values (8)69392
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter199502
69.7%
Space Separator43370
 
15.2%
Decimal Number34696
 
12.1%
Dash Punctuation8674
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S34696
17.4%
T26022
13.0%
L26022
13.0%
E17348
8.7%
D17348
8.7%
N17348
8.7%
R17348
8.7%
O8674
 
4.3%
C8674
 
4.3%
I8674
 
4.3%
Other values (2)17348
8.7%
Decimal Number
ValueCountFrequency (%)
28674
25.0%
88674
25.0%
78674
25.0%
58674
25.0%
Space Separator
ValueCountFrequency (%)
43370
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin199502
69.7%
Common86740
30.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S34696
17.4%
T26022
13.0%
L26022
13.0%
E17348
8.7%
D17348
8.7%
N17348
8.7%
R17348
8.7%
O8674
 
4.3%
C8674
 
4.3%
I8674
 
4.3%
Other values (2)17348
8.7%
Common
ValueCountFrequency (%)
43370
50.0%
-8674
 
10.0%
28674
 
10.0%
88674
 
10.0%
78674
 
10.0%
58674
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII286242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43370
15.2%
S34696
12.1%
T26022
 
9.1%
L26022
 
9.1%
E17348
 
6.1%
D17348
 
6.1%
N17348
 
6.1%
R17348
 
6.1%
O8674
 
3.0%
C8674
 
3.0%
Other values (8)69392
24.2%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Arizona
8674 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters60718
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona8674
100.0%

Length

2022-10-20T19:31:01.771683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:58.739223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:01.842178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona8674
100.0%

Most occurring characters

ValueCountFrequency (%)
A8674
14.3%
r8674
14.3%
i8674
14.3%
z8674
14.3%
o8674
14.3%
n8674
14.3%
a8674
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52044
85.7%
Uppercase Letter8674
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r8674
16.7%
i8674
16.7%
z8674
16.7%
o8674
16.7%
n8674
16.7%
a8674
16.7%
Uppercase Letter
ValueCountFrequency (%)
A8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60718
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A8674
14.3%
r8674
14.3%
i8674
14.3%
z8674
14.3%
o8674
14.3%
n8674
14.3%
a8674
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII60718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A8674
14.3%
r8674
14.3%
i8674
14.3%
z8674
14.3%
o8674
14.3%
n8674
14.3%
a8674
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Maricopa
8674 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69392
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa8674
100.0%

Length

2022-10-20T18:52:58.859674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona8674
100.0%

Most occurring characters

ValueCountFrequency (%)
A8674
14.3%
r8674
14.3%
i8674
14.3%
z8674
14.3%
o8674
14.3%
n8674
14.3%
a8674
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52044
85.7%
Uppercase Letter8674
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r8674
16.7%
i8674
16.7%
z8674
16.7%
o8674
16.7%
n8674
16.7%
a8674
16.7%
Uppercase Letter
ValueCountFrequency (%)
A8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60718
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A8674
14.3%
r8674
14.3%
i8674
14.3%
z8674
14.3%
o8674
14.3%
n8674
14.3%
a8674
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII60718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A8674
14.3%
r8674
14.3%
i8674
14.3%
z8674
14.3%
o8674
14.3%
n8674
14.3%
a8674
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Maricopa
8674 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69392
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa8674
100.0%

Length

2022-10-20T19:31:01.900928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:58.998914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:01.972752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
maricopa8674
100.0%

Most occurring characters

ValueCountFrequency (%)
a17348
25.0%
M8674
12.5%
r8674
12.5%
i8674
12.5%
c8674
12.5%
o8674
12.5%
p8674
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60718
87.5%
Uppercase Letter8674
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a17348
28.6%
r8674
14.3%
i8674
14.3%
c8674
14.3%
o8674
14.3%
p8674
14.3%
Uppercase Letter
ValueCountFrequency (%)
M8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin69392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a17348
25.0%
M8674
12.5%
r8674
12.5%
i8674
12.5%
c8674
12.5%
o8674
12.5%
p8674
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII69392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a17348
25.0%
M8674
12.5%
r8674
12.5%
i8674
12.5%
c8674
12.5%
o8674
12.5%
p8674
12.5%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Scottsdale
8674 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters86740
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScottsdale
2nd rowScottsdale
3rd rowScottsdale
4th rowScottsdale
5th rowScottsdale

Common Values

ValueCountFrequency (%)
Scottsdale8674
100.0%

Length

2022-10-20T18:52:59.116819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
maricopa8674
100.0%

Most occurring characters

ValueCountFrequency (%)
a17348
25.0%
M8674
12.5%
r8674
12.5%
i8674
12.5%
c8674
12.5%
o8674
12.5%
p8674
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60718
87.5%
Uppercase Letter8674
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a17348
28.6%
r8674
14.3%
i8674
14.3%
c8674
14.3%
o8674
14.3%
p8674
14.3%
Uppercase Letter
ValueCountFrequency (%)
M8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin69392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a17348
25.0%
M8674
12.5%
r8674
12.5%
i8674
12.5%
c8674
12.5%
o8674
12.5%
p8674
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII69392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a17348
25.0%
M8674
12.5%
r8674
12.5%
i8674
12.5%
c8674
12.5%
o8674
12.5%
p8674
12.5%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Scottsdale
8674 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters86740
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScottsdale
2nd rowScottsdale
3rd rowScottsdale
4th rowScottsdale
5th rowScottsdale

Common Values

ValueCountFrequency (%)
Scottsdale8674
100.0%

Length

2022-10-20T19:31:02.032717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:59.265135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:02.104959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
scottsdale8674
100.0%

Most occurring characters

ValueCountFrequency (%)
t17348
20.0%
S8674
10.0%
c8674
10.0%
o8674
10.0%
s8674
10.0%
d8674
10.0%
a8674
10.0%
l8674
10.0%
e8674
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter78066
90.0%
Uppercase Letter8674
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t17348
22.2%
c8674
11.1%
o8674
11.1%
s8674
11.1%
d8674
11.1%
a8674
11.1%
l8674
11.1%
e8674
11.1%
Uppercase Letter
ValueCountFrequency (%)
S8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin86740
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t17348
20.0%
S8674
10.0%
c8674
10.0%
o8674
10.0%
s8674
10.0%
d8674
10.0%
a8674
10.0%
l8674
10.0%
e8674
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII86740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t17348
20.0%
S8674
10.0%
c8674
10.0%
o8674
10.0%
s8674
10.0%
d8674
10.0%
a8674
10.0%
l8674
10.0%
e8674
10.0%
Distinct2176
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Minimum2000-01-01 00:00:00
Maximum2010-12-31 00:00:00
2022-10-20T18:52:59.404185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
scottsdale8674
100.0%

Most occurring characters

ValueCountFrequency (%)
t17348
20.0%
S8674
10.0%
c8674
10.0%
o8674
10.0%
s8674
10.0%
d8674
10.0%
a8674
10.0%
l8674
10.0%
e8674
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter78066
90.0%
Uppercase Letter8674
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t17348
22.2%
c8674
11.1%
o8674
11.1%
s8674
11.1%
d8674
11.1%
a8674
11.1%
l8674
11.1%
e8674
11.1%
Uppercase Letter
ValueCountFrequency (%)
S8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin86740
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t17348
20.0%
S8674
10.0%
c8674
10.0%
o8674
10.0%
s8674
10.0%
d8674
10.0%
a8674
10.0%
l8674
10.0%
e8674
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII86740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t17348
20.0%
S8674
10.0%
c8674
10.0%
o8674
10.0%
s8674
10.0%
d8674
10.0%
a8674
10.0%
l8674
10.0%
e8674
10.0%
Distinct2176
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Minimum2000-01-01 00:00:00
Maximum2010-12-31 00:00:00
2022-10-20T19:31:02.179609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:52:59.593288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:31:02.292259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per billion
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion8674
100.0%

Length

2022-10-20T18:52:59.749061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per billion
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion8674
100.0%

Length

2022-10-20T19:31:02.380601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:59.884247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:02.459280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts8674
33.3%
per8674
33.3%
billion8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121436
82.4%
Space Separator17348
 
11.8%
Uppercase Letter8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r17348
14.3%
i17348
14.3%
l17348
14.3%
a8674
7.1%
t8674
7.1%
s8674
7.1%
p8674
7.1%
e8674
7.1%
b8674
7.1%
o8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin130110
88.2%
Common17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r17348
13.3%
i17348
13.3%
l17348
13.3%
P8674
6.7%
a8674
6.7%
t8674
6.7%
s8674
6.7%
p8674
6.7%
e8674
6.7%
b8674
6.7%
Other values (2)17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1152
Distinct (%)0.13281069863961265
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean22.61699423656906
Minimum0.0
Maximum139.541667
Zeros4
Zeros (%)0.0004611482591653217
Memory size69520
2022-10-20T18:52:59.997940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts8674
33.3%
per8674
33.3%
billion8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121436
82.4%
Space Separator17348
 
11.8%
Uppercase Letter8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r17348
14.3%
i17348
14.3%
l17348
14.3%
a8674
7.1%
t8674
7.1%
s8674
7.1%
p8674
7.1%
e8674
7.1%
b8674
7.1%
o8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin130110
88.2%
Common17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r17348
13.3%
i17348
13.3%
l17348
13.3%
P8674
6.7%
a8674
6.7%
t8674
6.7%
s8674
6.7%
p8674
6.7%
e8674
6.7%
b8674
6.7%
Other values (2)17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1152
Distinct (%)0.13281069863961265
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean22.61699423656906
Minimum0.0
Maximum139.541667
Zeros4
Zeros (%)0.0004611482591653217
Memory size69520
2022-10-20T19:31:02.524360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.833333
Q116.458333
median21.666667
Q326.958333
95-th percentile35.17833355
Maximum139.541667
Range139.541667
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation11.64191646
Coefficient of variation (CV)0.514741983
Kurtosis26.08944896
Mean22.61699424
Median Absolute Deviation (MAD)5.25
Skewness3.717748036
Sum196179.808
Variance135.5342189
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.81690497 × 10-11
2022-10-20T18:53:00.188209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.833333
Q116.458333
median21.666667
Q326.958333
95-th percentile35.17833355
Maximum139.541667
Range139.541667
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation11.64191646
Coefficient of variation (CV)0.514741983
Kurtosis26.08944896
Mean22.61699424
Median Absolute Deviation (MAD)5.25
Skewness3.717748036
Sum196179.808
Variance135.5342189
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.81690497 × 10-11
2022-10-20T19:31:02.619516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.58333344
 
0.5%
20.33333336
 
0.4%
18.16666732
 
0.4%
19.16666732
 
0.4%
1732
 
0.4%
20.70833332
 
0.4%
19.04166732
 
0.4%
19.20833332
 
0.4%
24.16666728
 
0.3%
20.95833328
 
0.3%
Other values (1142)8346
96.2%
ValueCountFrequency (%)
04
< 0.1%
0.54
< 0.1%
1.5555564
< 0.1%
24
< 0.1%
2.7619052
< 0.1%
3.2222224
< 0.1%
3.5833334
< 0.1%
3.6086964
< 0.1%
3.6111114
< 0.1%
3.6254
< 0.1%
ValueCountFrequency (%)
139.5416674
< 0.1%
135.3333334
< 0.1%
135.18754
< 0.1%
123.3333334
< 0.1%
113.0833334
< 0.1%
110.1363644
< 0.1%
107.5454554
< 0.1%
105.54
< 0.1%
98.754
< 0.1%
97.4583334
< 0.1%
2022-10-20T18:53:00.773708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.58333344
 
0.5%
20.33333336
 
0.4%
18.16666732
 
0.4%
19.16666732
 
0.4%
1732
 
0.4%
20.70833332
 
0.4%
19.04166732
 
0.4%
19.20833332
 
0.4%
24.16666728
 
0.3%
20.95833328
 
0.3%
Other values (1142)8346
96.2%
ValueCountFrequency (%)
04
< 0.1%
0.54
< 0.1%
1.5555564
< 0.1%
24
< 0.1%
2.7619052
< 0.1%
3.2222224
< 0.1%
3.5833334
< 0.1%
3.6086964
< 0.1%
3.6111114
< 0.1%
3.6254
< 0.1%
ValueCountFrequency (%)
139.5416674
< 0.1%
135.3333334
< 0.1%
135.18754
< 0.1%
123.3333334
< 0.1%
113.0833334
< 0.1%
110.1363644
< 0.1%
107.5454554
< 0.1%
105.54
< 0.1%
98.754
< 0.1%
97.4583334
< 0.1%
2022-10-20T19:31:02.780465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct121
Distinct (%)0.01394973483975098
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean45.715932672354164
Minimum0.0
Maximum267.0
Zeros4
Zeros (%)0.0004611482591653217
Memory size69520
2022-10-20T18:53:01.246842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct121
Distinct (%)0.01394973483975098
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean45.715932672354164
Minimum0.0
Maximum267.0
Zeros4
Zeros (%)0.0004611482591653217
Memory size69520
2022-10-20T19:31:02.910691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q136
median44
Q351
95-th percentile63
Maximum267
Range267
Interquartile range (IQR)15

Descriptive statistics

Standard deviation24.2228595
Coefficient of variation (CV)0.5298559624
Kurtosis30.91678884
Mean45.71593267
Median Absolute Deviation (MAD)7
Skewness4.809530262
Sum396540
Variance586.7469224
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.383299232 × 10-10
2022-10-20T18:53:01.442927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q136
median44
Q351
95-th percentile63
Maximum267
Range267
Interquartile range (IQR)15

Descriptive statistics

Standard deviation24.2228595
Coefficient of variation (CV)0.5298559624
Kurtosis30.91678884
Mean45.71593267
Median Absolute Deviation (MAD)7
Skewness4.809530262
Sum396540
Variance586.7469224
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.383299232 × 10-10
2022-10-20T19:31:03.004514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46372
 
4.3%
47342
 
3.9%
50334
 
3.9%
41330
 
3.8%
45328
 
3.8%
43306
 
3.5%
44300
 
3.5%
48298
 
3.4%
42282
 
3.3%
39268
 
3.1%
Other values (111)5514
63.6%
ValueCountFrequency (%)
04
 
< 0.1%
24
 
< 0.1%
716
0.2%
88
 
0.1%
916
0.2%
1018
0.2%
1224
0.3%
1320
0.2%
148
 
0.1%
1516
0.2%
ValueCountFrequency (%)
2674
< 0.1%
2564
< 0.1%
2444
< 0.1%
2414
< 0.1%
2334
< 0.1%
2314
< 0.1%
2294
< 0.1%
2254
< 0.1%
2244
< 0.1%
2234
< 0.1%
2022-10-20T18:53:01.744466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46372
 
4.3%
47342
 
3.9%
50334
 
3.9%
41330
 
3.8%
45328
 
3.8%
43306
 
3.5%
44300
 
3.5%
48298
 
3.4%
42282
 
3.3%
39268
 
3.1%
Other values (111)5514
63.6%
ValueCountFrequency (%)
04
 
< 0.1%
24
 
< 0.1%
716
0.2%
88
 
0.1%
916
0.2%
1018
0.2%
1224
0.3%
1320
0.2%
148
 
0.1%
1516
0.2%
ValueCountFrequency (%)
2674
< 0.1%
2564
< 0.1%
2444
< 0.1%
2414
< 0.1%
2334
< 0.1%
2314
< 0.1%
2294
< 0.1%
2254
< 0.1%
2244
< 0.1%
2234
< 0.1%
2022-10-20T19:31:03.178566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.00276688955499193
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean16.267465990315888
Minimum0
Maximum23
Zeros660
Zeros (%)0.07608946276227807
Memory size69520
2022-10-20T18:53:01.984254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.00276688955499193
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean16.267465990315888
Minimum0
Maximum23
Zeros660
Zeros (%)0.07608946276227807
Memory size69520
2022-10-20T19:31:03.305396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median19
Q320
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.41857782
Coefficient of variation (CV)0.3945653136
Kurtosis1.178946483
Mean16.26746599
Median Absolute Deviation (MAD)1
Skewness-1.605083343
Sum141104
Variance41.19814123
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.122748134 × 10-25
2022-10-20T18:53:02.142598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median19
Q320
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.41857782
Coefficient of variation (CV)0.3945653136
Kurtosis1.178946483
Mean16.26746599
Median Absolute Deviation (MAD)1
Skewness-1.605083343
Sum141104
Variance41.19814123
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.122748134 × 10-25
2022-10-20T19:31:03.392216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
191942
22.4%
181880
21.7%
201452
16.7%
21760
 
8.8%
0660
 
7.6%
17340
 
3.9%
7296
 
3.4%
22294
 
3.4%
23218
 
2.5%
8204
 
2.4%
Other values (14)628
 
7.2%
ValueCountFrequency (%)
0660
7.6%
184
 
1.0%
256
 
0.6%
312
 
0.1%
416
 
0.2%
536
 
0.4%
6192
 
2.2%
7296
3.4%
8204
 
2.4%
964
 
0.7%
ValueCountFrequency (%)
23218
 
2.5%
22294
 
3.4%
21760
 
8.8%
201452
16.7%
191942
22.4%
181880
21.7%
17340
 
3.9%
1636
 
0.4%
1520
 
0.2%
1416
 
0.2%
2022-10-20T18:53:02.401184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
191942
22.4%
181880
21.7%
201452
16.7%
21760
 
8.8%
0660
 
7.6%
17340
 
3.9%
7296
 
3.4%
22294
 
3.4%
23218
 
2.5%
8204
 
2.4%
Other values (14)628
 
7.2%
ValueCountFrequency (%)
0660
7.6%
184
 
1.0%
256
 
0.6%
312
 
0.1%
416
 
0.2%
536
 
0.4%
6192
 
2.2%
7296
3.4%
8204
 
2.4%
964
 
0.7%
ValueCountFrequency (%)
23218
 
2.5%
22294
 
3.4%
21760
 
8.8%
201452
16.7%
191942
22.4%
181880
21.7%
17340
 
3.9%
1636
 
0.4%
1520
 
0.2%
1416
 
0.2%
2022-10-20T19:31:03.583259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct98
Distinct (%)0.01129813234955038
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean41.99146875720544
Minimum0
Maximum132
Zeros4
Zeros (%)0.0004611482591653217
Memory size69520
2022-10-20T18:53:02.649843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct98
Distinct (%)0.01129813234955038
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean41.99146875720544
Minimum0
Maximum132
Zeros4
Zeros (%)0.0004611482591653217
Memory size69520
2022-10-20T19:31:03.728851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q134
median42
Q348
95-th percentile61
Maximum132
Range132
Interquartile range (IQR)14

Descriptive statistics

Standard deviation15.82272865
Coefficient of variation (CV)0.3768081736
Kurtosis9.595034018
Mean41.99146876
Median Absolute Deviation (MAD)7
Skewness2.229427033
Sum364234
Variance250.3587419
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.365294294 × 10-9
2022-10-20T18:53:02.874121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q134
median42
Q348
95-th percentile61
Maximum132
Range132
Interquartile range (IQR)14

Descriptive statistics

Standard deviation15.82272865
Coefficient of variation (CV)0.3768081736
Kurtosis9.595034018
Mean41.99146876
Median Absolute Deviation (MAD)7
Skewness2.229427033
Sum364234
Variance250.3587419
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.365294294 × 10-9
2022-10-20T19:31:03.825114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42628
 
7.2%
43372
 
4.3%
44342
 
3.9%
47334
 
3.9%
39330
 
3.8%
41306
 
3.5%
45298
 
3.4%
40282
 
3.3%
37268
 
3.1%
46268
 
3.1%
Other values (88)5246
60.5%
ValueCountFrequency (%)
04
 
< 0.1%
24
 
< 0.1%
716
 
0.2%
824
0.3%
918
0.2%
1124
0.3%
1220
0.2%
138
 
0.1%
1416
 
0.2%
1542
0.5%
ValueCountFrequency (%)
1324
 
< 0.1%
1304
 
< 0.1%
1284
 
< 0.1%
1274
 
< 0.1%
1268
0.1%
1254
 
< 0.1%
12416
0.2%
12312
0.1%
1218
0.1%
1204
 
< 0.1%
2022-10-20T18:53:03.204458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42628
 
7.2%
43372
 
4.3%
44342
 
3.9%
47334
 
3.9%
39330
 
3.8%
41306
 
3.5%
45298
 
3.4%
40282
 
3.3%
37268
 
3.1%
46268
 
3.1%
Other values (88)5246
60.5%
ValueCountFrequency (%)
04
 
< 0.1%
24
 
< 0.1%
716
 
0.2%
824
0.3%
918
0.2%
1124
0.3%
1220
0.2%
138
 
0.1%
1416
 
0.2%
1542
0.5%
ValueCountFrequency (%)
1324
 
< 0.1%
1304
 
< 0.1%
1284
 
< 0.1%
1274
 
< 0.1%
1268
0.1%
1254
 
< 0.1%
12416
0.2%
12312
0.1%
1218
0.1%
1204
 
< 0.1%
2022-10-20T19:31:03.999739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per million
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million8674
100.0%

Length

2022-10-20T18:53:03.453247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per million
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million8674
100.0%

Length

2022-10-20T19:31:04.134054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:03.588340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:04.206184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts8674
33.3%
per8674
33.3%
million8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121436
82.4%
Space Separator17348
 
11.8%
Uppercase Letter8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r17348
14.3%
i17348
14.3%
l17348
14.3%
a8674
7.1%
t8674
7.1%
s8674
7.1%
p8674
7.1%
e8674
7.1%
m8674
7.1%
o8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin130110
88.2%
Common17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r17348
13.3%
i17348
13.3%
l17348
13.3%
P8674
6.7%
a8674
6.7%
t8674
6.7%
s8674
6.7%
p8674
6.7%
e8674
6.7%
m8674
6.7%
Other values (2)17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct854
Distinct (%)0.09845515333179618
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.01910428821766198
Minimum0.001
Maximum0.045944
Zeros0
Zeros (%)0.0
Memory size69520
2022-10-20T18:53:03.710590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts8674
33.3%
per8674
33.3%
million8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121436
82.4%
Space Separator17348
 
11.8%
Uppercase Letter8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r17348
14.3%
i17348
14.3%
l17348
14.3%
a8674
7.1%
t8674
7.1%
s8674
7.1%
p8674
7.1%
e8674
7.1%
m8674
7.1%
o8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin130110
88.2%
Common17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r17348
13.3%
i17348
13.3%
l17348
13.3%
P8674
6.7%
a8674
6.7%
t8674
6.7%
s8674
6.7%
p8674
6.7%
e8674
6.7%
m8674
6.7%
Other values (2)17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct854
Distinct (%)0.09845515333179618
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.01910428821766198
Minimum0.001
Maximum0.045944
Zeros0
Zeros (%)0.0
Memory size69520
2022-10-20T19:31:04.268167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.006737
Q10.012333
median0.018211
Q30.025042
95-th percentile0.034375
Maximum0.045944
Range0.044944
Interquartile range (IQR)0.012709

Descriptive statistics

Standard deviation0.008581388968
Coefficient of variation (CV)0.4491865318
Kurtosis-0.3621372535
Mean0.01910428822
Median Absolute Deviation (MAD)0.006206
Skewness0.432347019
Sum165.710596
Variance7.364023661 × 10-5
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.838970721 × 10-6
2022-10-20T18:53:03.969634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.006737
Q10.012333
median0.018211
Q30.025042
95-th percentile0.034375
Maximum0.045944
Range0.044944
Interquartile range (IQR)0.012709

Descriptive statistics

Standard deviation0.008581388968
Coefficient of variation (CV)0.4491865318
Kurtosis-0.3621372535
Mean0.01910428822
Median Absolute Deviation (MAD)0.006206
Skewness0.432347019
Sum165.710596
Variance7.364023661 × 10-5
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.838970721 × 10-6
2022-10-20T19:31:04.363255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01116752
 
0.6%
0.0142540
 
0.5%
0.00929232
 
0.4%
0.01629232
 
0.4%
0.01737532
 
0.4%
0.00866732
 
0.4%
0.02012532
 
0.4%
0.02037532
 
0.4%
0.02029228
 
0.3%
0.02691728
 
0.3%
Other values (844)8334
96.1%
ValueCountFrequency (%)
0.0014
< 0.1%
0.0011674
< 0.1%
0.0019174
< 0.1%
0.0019584
< 0.1%
0.0021254
< 0.1%
0.0023894
< 0.1%
0.00254
< 0.1%
0.0026254
< 0.1%
0.002754
< 0.1%
0.0028754
< 0.1%
ValueCountFrequency (%)
0.0459444
< 0.1%
0.0458754
< 0.1%
0.0458334
< 0.1%
0.0451674
< 0.1%
0.0450422
< 0.1%
0.0448334
< 0.1%
0.0447924
< 0.1%
0.0443754
< 0.1%
0.0441254
< 0.1%
0.0432084
< 0.1%
2022-10-20T18:53:04.534667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01116752
 
0.6%
0.0142540
 
0.5%
0.00929232
 
0.4%
0.01629232
 
0.4%
0.01737532
 
0.4%
0.00866732
 
0.4%
0.02012532
 
0.4%
0.02037532
 
0.4%
0.02029228
 
0.3%
0.02691728
 
0.3%
Other values (844)8334
96.1%
ValueCountFrequency (%)
0.0014
< 0.1%
0.0011674
< 0.1%
0.0019174
< 0.1%
0.0019584
< 0.1%
0.0021254
< 0.1%
0.0023894
< 0.1%
0.00254
< 0.1%
0.0026254
< 0.1%
0.002754
< 0.1%
0.0028754
< 0.1%
ValueCountFrequency (%)
0.0459444
< 0.1%
0.0458754
< 0.1%
0.0458334
< 0.1%
0.0451674
< 0.1%
0.0450422
< 0.1%
0.0448334
< 0.1%
0.0447924
< 0.1%
0.0443754
< 0.1%
0.0441254
< 0.1%
0.0432084
< 0.1%
2022-10-20T19:31:04.534659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct76
Distinct (%)0.008761816924141111
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.03657020982245792
Minimum0.001
Maximum0.078
Zeros0
Zeros (%)0.0
Memory size69520
2022-10-20T18:53:04.797163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct76
Distinct (%)0.008761816924141111
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.03657020982245792
Minimum0.001
Maximum0.078
Zeros0
Zeros (%)0.0
Memory size69520
2022-10-20T19:31:04.681118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.016
Q10.028
median0.036
Q30.045
95-th percentile0.057
Maximum0.078
Range0.077
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.01237206055
Coefficient of variation (CV)0.3383098048
Kurtosis-0.01707873162
Mean0.03657020982
Median Absolute Deviation (MAD)0.008
Skewness0.1098211007
Sum317.21
Variance0.0001530678822
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.157927082 × 10-5
2022-10-20T18:53:04.977014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.016
Q10.028
median0.036
Q30.045
95-th percentile0.057
Maximum0.078
Range0.077
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.01237206055
Coefficient of variation (CV)0.3383098048
Kurtosis-0.01707873162
Mean0.03657020982
Median Absolute Deviation (MAD)0.008
Skewness0.1098211007
Sum317.21
Variance0.0001530678822
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.157927082 × 10-5
2022-10-20T19:31:04.784823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.039350
 
4.0%
0.035320
 
3.7%
0.031304
 
3.5%
0.037292
 
3.4%
0.033284
 
3.3%
0.032280
 
3.2%
0.034274
 
3.2%
0.038264
 
3.0%
0.03264
 
3.0%
0.042258
 
3.0%
Other values (66)5784
66.7%
ValueCountFrequency (%)
0.0014
 
< 0.1%
0.0024
 
< 0.1%
0.00312
 
0.1%
0.0048
 
0.1%
0.0054
 
< 0.1%
0.00612
 
0.1%
0.00728
0.3%
0.00812
 
0.1%
0.00936
0.4%
0.014
 
< 0.1%
ValueCountFrequency (%)
0.0788
 
0.1%
0.0774
 
< 0.1%
0.0754
 
< 0.1%
0.0734
 
< 0.1%
0.0724
 
< 0.1%
0.07116
0.2%
0.076
 
0.1%
0.0698
 
0.1%
0.06820
0.2%
0.0678
 
0.1%
2022-10-20T18:53:05.261809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.039350
 
4.0%
0.035320
 
3.7%
0.031304
 
3.5%
0.037292
 
3.4%
0.033284
 
3.3%
0.032280
 
3.2%
0.034274
 
3.2%
0.038264
 
3.0%
0.03264
 
3.0%
0.042258
 
3.0%
Other values (66)5784
66.7%
ValueCountFrequency (%)
0.0014
 
< 0.1%
0.0024
 
< 0.1%
0.00312
 
0.1%
0.0048
 
0.1%
0.0054
 
< 0.1%
0.00612
 
0.1%
0.00728
0.3%
0.00812
 
0.1%
0.00936
0.4%
0.014
 
< 0.1%
ValueCountFrequency (%)
0.0788
 
0.1%
0.0774
 
< 0.1%
0.0754
 
< 0.1%
0.0734
 
< 0.1%
0.0724
 
< 0.1%
0.07116
0.2%
0.076
 
0.1%
0.0698
 
0.1%
0.06820
0.2%
0.0678
 
0.1%
2022-10-20T19:31:04.971203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.0026516024902005996
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean9.88724925063408
Minimum0
Maximum23
Zeros148
Zeros (%)0.0170624855891169
Memory size69520
2022-10-20T18:53:05.517587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.0026516024902005996
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean9.88724925063408
Minimum0
Maximum23
Zeros148
Zeros (%)0.0170624855891169
Memory size69520
2022-10-20T19:31:05.712652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q19
median10
Q310
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.669628164
Coefficient of variation (CV)0.2700071675
Kurtosis12.37047601
Mean9.887249251
Median Absolute Deviation (MAD)1
Skewness1.685977999
Sum85762
Variance7.126914536
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.316671286 × 10-23
2022-10-20T18:53:05.670538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q19
median10
Q310
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.669628164
Coefficient of variation (CV)0.2700071675
Kurtosis12.37047601
Mean9.887249251
Median Absolute Deviation (MAD)1
Skewness1.685977999
Sum85762
Variance7.126914536
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.316671286 × 10-23
2022-10-20T19:31:05.793718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
103580
41.3%
92818
32.5%
11974
 
11.2%
8472
 
5.4%
12174
 
2.0%
0148
 
1.7%
2380
 
0.9%
2260
 
0.7%
756
 
0.6%
1354
 
0.6%
Other values (13)258
 
3.0%
ValueCountFrequency (%)
0148
 
1.7%
112
 
0.1%
28
 
0.1%
44
 
< 0.1%
58
 
0.1%
644
 
0.5%
756
 
0.6%
8472
 
5.4%
92818
32.5%
103580
41.3%
ValueCountFrequency (%)
2380
0.9%
2260
0.7%
2152
0.6%
2040
0.5%
1936
0.4%
1810
 
0.1%
1712
 
0.1%
164
 
< 0.1%
154
 
< 0.1%
1424
 
0.3%
2022-10-20T18:53:06.660541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
103580
41.3%
92818
32.5%
11974
 
11.2%
8472
 
5.4%
12174
 
2.0%
0148
 
1.7%
2380
 
0.9%
2260
 
0.7%
756
 
0.6%
1354
 
0.6%
Other values (13)258
 
3.0%
ValueCountFrequency (%)
0148
 
1.7%
112
 
0.1%
28
 
0.1%
44
 
< 0.1%
58
 
0.1%
644
 
0.5%
756
 
0.6%
8472
 
5.4%
92818
32.5%
103580
41.3%
ValueCountFrequency (%)
2380
0.9%
2260
0.7%
2152
0.6%
2040
0.5%
1936
0.4%
1810
 
0.1%
1712
 
0.1%
164
 
< 0.1%
154
 
< 0.1%
1424
 
0.3%
2022-10-20T19:31:05.957468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct67
Distinct (%)0.007724233341019138
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean31.424948120820844
Minimum1
Maximum106
Zeros0
Zeros (%)0.0
Memory size69520
2022-10-20T18:53:06.907031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct67
Distinct (%)0.007724233341019138
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean31.424948120820844
Minimum1
Maximum106
Zeros0
Zeros (%)0.0
Memory size69520
2022-10-20T19:31:06.095813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q124
median31
Q338
95-th percentile48
Maximum106
Range105
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.90341062
Coefficient of variation (CV)0.3787885526
Kurtosis4.494916953
Mean31.42494812
Median Absolute Deviation (MAD)7
Skewness1.128057172
Sum272580
Variance141.6911843
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.711969553 × 10-6
2022-10-20T18:53:07.083555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q124
median31
Q338
95-th percentile48
Maximum106
Range105
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.90341062
Coefficient of variation (CV)0.3787885526
Kurtosis4.494916953
Mean31.42494812
Median Absolute Deviation (MAD)7
Skewness1.128057172
Sum272580
Variance141.6911843
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.711969553 × 10-6
2022-10-20T19:31:06.197152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31544
 
6.3%
25484
 
5.6%
36472
 
5.4%
42350
 
4.0%
33350
 
4.0%
30320
 
3.7%
26304
 
3.5%
28284
 
3.3%
27280
 
3.2%
29274
 
3.2%
Other values (57)5012
57.8%
ValueCountFrequency (%)
14
 
< 0.1%
24
 
< 0.1%
320
0.2%
44
 
< 0.1%
512
 
0.1%
628
0.3%
712
 
0.1%
840
0.5%
940
0.5%
1040
0.5%
ValueCountFrequency (%)
1068
 
0.1%
1044
 
< 0.1%
1004
 
< 0.1%
934
 
< 0.1%
904
 
< 0.1%
8716
0.2%
846
 
0.1%
808
 
0.1%
7720
0.2%
748
 
0.1%
2022-10-20T18:53:07.446960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31544
 
6.3%
25484
 
5.6%
36472
 
5.4%
42350
 
4.0%
33350
 
4.0%
30320
 
3.7%
26304
 
3.5%
28284
 
3.3%
27280
 
3.2%
29274
 
3.2%
Other values (57)5012
57.8%
ValueCountFrequency (%)
14
 
< 0.1%
24
 
< 0.1%
320
0.2%
44
 
< 0.1%
512
 
0.1%
628
0.3%
712
 
0.1%
840
0.5%
940
0.5%
1040
0.5%
ValueCountFrequency (%)
1068
 
0.1%
1044
 
< 0.1%
1004
 
< 0.1%
934
 
< 0.1%
904
 
< 0.1%
8716
0.2%
846
 
0.1%
808
 
0.1%
7720
0.2%
748
 
0.1%
2022-10-20T19:31:06.403622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per billion
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion8674
100.0%

Length

2022-10-20T18:53:07.741753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per billion
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion8674
100.0%

Length

2022-10-20T19:31:06.535232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:07.901713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:06.616028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts8674
33.3%
per8674
33.3%
billion8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121436
82.4%
Space Separator17348
 
11.8%
Uppercase Letter8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r17348
14.3%
i17348
14.3%
l17348
14.3%
a8674
7.1%
t8674
7.1%
s8674
7.1%
p8674
7.1%
e8674
7.1%
b8674
7.1%
o8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin130110
88.2%
Common17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r17348
13.3%
i17348
13.3%
l17348
13.3%
P8674
6.7%
a8674
6.7%
t8674
6.7%
s8674
6.7%
p8674
6.7%
e8674
6.7%
b8674
6.7%
Other values (2)17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct876
Distinct (%)0.10099146875720544
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.5732558598109292
Minimum0.0
Maximum19.375
Zeros218
Zeros (%)0.02513258012451003
Memory size69520
2022-10-20T18:53:08.022372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts8674
33.3%
per8674
33.3%
billion8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121436
82.4%
Space Separator17348
 
11.8%
Uppercase Letter8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r17348
14.3%
i17348
14.3%
l17348
14.3%
a8674
7.1%
t8674
7.1%
s8674
7.1%
p8674
7.1%
e8674
7.1%
b8674
7.1%
o8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin130110
88.2%
Common17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r17348
13.3%
i17348
13.3%
l17348
13.3%
P8674
6.7%
a8674
6.7%
t8674
6.7%
s8674
6.7%
p8674
6.7%
e8674
6.7%
b8674
6.7%
Other values (2)17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct876
Distinct (%)0.10099146875720544
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.5732558598109292
Minimum0.0
Maximum19.375
Zeros218
Zeros (%)0.02513258012451003
Memory size69520
2022-10-20T19:31:06.683424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1125
Q10.775
median1.2375
Q32.125
95-th percentile3.9143114
Maximum19.375
Range19.375
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation1.35930361
Coefficient of variation (CV)0.8640067038
Kurtosis36.14411251
Mean1.57325586
Median Absolute Deviation (MAD)0.6125
Skewness4.033779497
Sum13646.42133
Variance1.847706303
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.169279767 × 10-14
2022-10-20T18:53:08.215292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1125
Q10.775
median1.2375
Q32.125
95-th percentile3.9143114
Maximum19.375
Range19.375
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation1.35930361
Coefficient of variation (CV)0.8640067038
Kurtosis36.14411251
Mean1.57325586
Median Absolute Deviation (MAD)0.6125
Skewness4.033779497
Sum13646.42133
Variance1.847706303
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.169279767 × 10-14
2022-10-20T19:31:06.786142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1388
 
4.5%
0218
 
2.5%
1.083333105
 
1.2%
298
 
1.1%
1.12588
 
1.0%
1.586
 
1.0%
1.04166784
 
1.0%
1.16666764
 
0.7%
0.95833364
 
0.7%
1.07563
 
0.7%
Other values (866)7416
85.5%
ValueCountFrequency (%)
0218
2.5%
0.037536
 
0.4%
0.04166736
 
0.4%
0.04285710
 
0.1%
0.0434784
 
< 0.1%
0.0454554
 
< 0.1%
0.0476192
 
< 0.1%
0.062
 
< 0.1%
0.0666672
 
< 0.1%
0.0714292
 
< 0.1%
ValueCountFrequency (%)
19.3752
< 0.1%
18.952
< 0.1%
17.0416672
< 0.1%
17.0252
< 0.1%
16.2916672
< 0.1%
16.26252
< 0.1%
15.7083332
< 0.1%
15.6752
< 0.1%
13.3333332
< 0.1%
13.31252
< 0.1%
2022-10-20T18:53:08.534857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1388
 
4.5%
0218
 
2.5%
1.083333105
 
1.2%
298
 
1.1%
1.12588
 
1.0%
1.586
 
1.0%
1.04166784
 
1.0%
1.16666764
 
0.7%
0.95833364
 
0.7%
1.07563
 
0.7%
Other values (866)7416
85.5%
ValueCountFrequency (%)
0218
2.5%
0.037536
 
0.4%
0.04166736
 
0.4%
0.04285710
 
0.1%
0.0434784
 
< 0.1%
0.0454554
 
< 0.1%
0.0476192
 
< 0.1%
0.062
 
< 0.1%
0.0666672
 
< 0.1%
0.0714292
 
< 0.1%
ValueCountFrequency (%)
19.3752
< 0.1%
18.952
< 0.1%
17.0416672
< 0.1%
17.0252
< 0.1%
16.2916672
< 0.1%
16.26252
< 0.1%
15.7083332
< 0.1%
15.6752
< 0.1%
13.3333332
< 0.1%
13.31252
< 0.1%
2022-10-20T19:31:06.947009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct47
Distinct (%)0.005418492045192529
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean2.9076550611021443
Minimum0.0
Maximum30.0
Zeros218
Zeros (%)0.02513258012451003
Memory size69520
2022-10-20T18:53:08.796977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct47
Distinct (%)0.005418492045192529
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean2.9076550611021443
Minimum0.0
Maximum30.0
Zeros218
Zeros (%)0.02513258012451003
Memory size69520
2022-10-20T19:31:07.075728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.3
median2.3
Q34
95-th percentile7
Maximum30
Range30
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation2.175143685
Coefficient of variation (CV)0.7480748712
Kurtosis16.1371465
Mean2.907655061
Median Absolute Deviation (MAD)1
Skewness2.678504312
Sum25221
Variance4.731250052
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.216947079 × 10-11
2022-10-20T18:53:08.977320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.3
median2.3
Q34
95-th percentile7
Maximum30
Range30
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation2.175143685
Coefficient of variation (CV)0.7480748712
Kurtosis16.1371465
Mean2.907655061
Median Absolute Deviation (MAD)1
Skewness2.678504312
Sum25221
Variance4.731250052
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.216947079 × 10-11
2022-10-20T19:31:07.178432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
21680
19.4%
11399
16.1%
31174
13.5%
4724
8.3%
5462
 
5.3%
1.3373
 
4.3%
1.6351
 
4.0%
2.3304
 
3.5%
2.6258
 
3.0%
6238
 
2.7%
Other values (37)1711
19.7%
ValueCountFrequency (%)
0218
 
2.5%
0.378
 
0.9%
0.6117
 
1.3%
11399
16.1%
1.3373
 
4.3%
1.6351
 
4.0%
21680
19.4%
2.3304
 
3.5%
2.6258
 
3.0%
31174
13.5%
ValueCountFrequency (%)
302
< 0.1%
224
< 0.1%
212
< 0.1%
20.34
< 0.1%
202
< 0.1%
19.64
< 0.1%
182
< 0.1%
172
< 0.1%
164
< 0.1%
152
< 0.1%
2022-10-20T18:53:09.352324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
21680
19.4%
11399
16.1%
31174
13.5%
4724
8.3%
5462
 
5.3%
1.3373
 
4.3%
1.6351
 
4.0%
2.3304
 
3.5%
2.6258
 
3.0%
6238
 
2.7%
Other values (37)1711
19.7%
ValueCountFrequency (%)
0218
 
2.5%
0.378
 
0.9%
0.6117
 
1.3%
11399
16.1%
1.3373
 
4.3%
1.6351
 
4.0%
21680
19.4%
2.3304
 
3.5%
2.6258
 
3.0%
31174
13.5%
ValueCountFrequency (%)
302
< 0.1%
224
< 0.1%
212
< 0.1%
20.34
< 0.1%
202
< 0.1%
19.64
< 0.1%
182
< 0.1%
172
< 0.1%
164
< 0.1%
152
< 0.1%
2022-10-20T19:31:07.372861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.00276688955499193
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean11.523172700023057
Minimum0
Maximum23
Zeros941
Zeros (%)0.10848512796864192
Memory size69520
2022-10-20T18:53:09.608091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.00276688955499193
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean11.523172700023057
Minimum0
Maximum23
Zeros941
Zeros (%)0.10848512796864192
Memory size69520
2022-10-20T19:31:07.502263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median11
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.715973288
Coefficient of variation (CV)0.6696049334
Kurtosis-1.409279841
Mean11.5231727
Median Absolute Deviation (MAD)8
Skewness-0.01539108531
Sum99952
Variance59.53624378
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.352598167 × 10-21
2022-10-20T18:53:09.752173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median11
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.715973288
Coefficient of variation (CV)0.6696049334
Kurtosis-1.409279841
Mean11.5231727
Median Absolute Deviation (MAD)8
Skewness-0.01539108531
Sum99952
Variance59.53624378
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.352598167 × 10-21
2022-10-20T19:31:07.581450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201457
16.8%
81143
13.2%
0941
10.8%
2885
10.2%
23635
7.3%
7610
7.0%
11512
 
5.9%
19434
 
5.0%
14305
 
3.5%
18276
 
3.2%
Other values (14)1476
17.0%
ValueCountFrequency (%)
0941
10.8%
158
 
0.7%
2885
10.2%
328
 
0.3%
434
 
0.4%
5102
 
1.2%
6222
 
2.6%
7610
7.0%
81143
13.2%
9150
 
1.7%
ValueCountFrequency (%)
23635
7.3%
2291
 
1.0%
21185
 
2.1%
201457
16.8%
19434
 
5.0%
18276
 
3.2%
17258
 
3.0%
1633
 
0.4%
1532
 
0.4%
14305
 
3.5%
2022-10-20T18:53:10.094585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201457
16.8%
81143
13.2%
0941
10.8%
2885
10.2%
23635
7.3%
7610
7.0%
11512
 
5.9%
19434
 
5.0%
14305
 
3.5%
18276
 
3.2%
Other values (14)1476
17.0%
ValueCountFrequency (%)
0941
10.8%
158
 
0.7%
2885
10.2%
328
 
0.3%
434
 
0.4%
5102
 
1.2%
6222
 
2.6%
7610
7.0%
81143
13.2%
9150
 
1.7%
ValueCountFrequency (%)
23635
7.3%
2291
 
1.0%
21185
 
2.1%
201457
16.8%
19434
 
5.0%
18276
 
3.2%
17258
 
3.0%
1633
 
0.4%
1532
 
0.4%
14305
 
3.5%
2022-10-20T19:31:07.773496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL
ZEROS

Distinct23
Distinct (%)0.005301982480405717
Missing4336
Missing (%)0.4998847129352087
Infinite0
Infinite (%)0.0
Mean4.538266482249885
Minimum0.0
Maximum43.0
Zeros108
Zeros (%)0.012451002997463684
Memory size69520
2022-10-20T18:53:10.339245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL
ZEROS

Distinct23
Distinct (%)0.005301982480405717
Missing4336
Missing (%)0.4998847129352087
Infinite0
Infinite (%)0.0
Mean4.538266482249885
Minimum0.0
Maximum43.0
Zeros108
Zeros (%)0.012451002997463684
Memory size69520
2022-10-20T19:31:07.905073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile10
Maximum43
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.44515137
Coefficient of variation (CV)0.7591337758
Kurtosis14.76428657
Mean4.538266482
Median Absolute Deviation (MAD)2
Skewness2.511087739
Sum19687
Variance11.86906796
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.745867097 × 10-8
2022-10-20T18:53:10.482175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile10
Maximum43
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.44515137
Coefficient of variation (CV)0.7591337758
Kurtosis14.76428657
Mean4.538266482
Median Absolute Deviation (MAD)2
Skewness2.511087739
Sum19687
Variance11.86906796
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.745867097 × 10-8
2022-10-20T19:31:07.983837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
31133
 
13.1%
4859
 
9.9%
1756
 
8.7%
6576
 
6.6%
7376
 
4.3%
9198
 
2.3%
10124
 
1.4%
0108
 
1.2%
1180
 
0.9%
1352
 
0.6%
Other values (13)76
 
0.9%
(Missing)4336
50.0%
ValueCountFrequency (%)
0108
 
1.2%
1756
8.7%
31133
13.1%
4859
9.9%
6576
6.6%
7376
 
4.3%
9198
 
2.3%
10124
 
1.4%
1180
 
0.9%
1352
 
0.6%
ValueCountFrequency (%)
432
 
< 0.1%
314
< 0.1%
302
 
< 0.1%
292
 
< 0.1%
262
 
< 0.1%
242
 
< 0.1%
232
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
196
0.1%
2022-10-20T18:53:10.809193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
31133
 
13.1%
4859
 
9.9%
1756
 
8.7%
6576
 
6.6%
7376
 
4.3%
9198
 
2.3%
10124
 
1.4%
0108
 
1.2%
1180
 
0.9%
1352
 
0.6%
Other values (13)76
 
0.9%
(Missing)4336
50.0%
ValueCountFrequency (%)
0108
 
1.2%
1756
8.7%
31133
13.1%
4859
9.9%
6576
6.6%
7376
 
4.3%
9198
 
2.3%
10124
 
1.4%
1180
 
0.9%
1352
 
0.6%
ValueCountFrequency (%)
432
 
< 0.1%
314
< 0.1%
302
 
< 0.1%
292
 
< 0.1%
262
 
< 0.1%
242
 
< 0.1%
232
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
196
0.1%
2022-10-20T19:31:08.128325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per million
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million8674
100.0%

Length

2022-10-20T18:53:11.087906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Parts per million
8674 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters147458
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million8674
100.0%

Length

2022-10-20T19:31:08.255514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:11.234380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:08.343061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts8674
33.3%
per8674
33.3%
million8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121436
82.4%
Space Separator17348
 
11.8%
Uppercase Letter8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r17348
14.3%
i17348
14.3%
l17348
14.3%
a8674
7.1%
t8674
7.1%
s8674
7.1%
p8674
7.1%
e8674
7.1%
m8674
7.1%
o8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin130110
88.2%
Common17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r17348
13.3%
i17348
13.3%
l17348
13.3%
P8674
6.7%
a8674
6.7%
t8674
6.7%
s8674
6.7%
p8674
6.7%
e8674
6.7%
m8674
6.7%
Other values (2)17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct815
Distinct (%)0.09395895780493428
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.5817473709937745
Minimum0.0
Maximum2.15
Zeros20
Zeros (%)0.002305741295826608
Memory size69520
2022-10-20T18:53:11.342376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts8674
33.3%
per8674
33.3%
million8674
33.3%

Most occurring characters

ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121436
82.4%
Space Separator17348
 
11.8%
Uppercase Letter8674
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r17348
14.3%
i17348
14.3%
l17348
14.3%
a8674
7.1%
t8674
7.1%
s8674
7.1%
p8674
7.1%
e8674
7.1%
m8674
7.1%
o8674
7.1%
Space Separator
ValueCountFrequency (%)
17348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P8674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin130110
88.2%
Common17348
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r17348
13.3%
i17348
13.3%
l17348
13.3%
P8674
6.7%
a8674
6.7%
t8674
6.7%
s8674
6.7%
p8674
6.7%
e8674
6.7%
m8674
6.7%
Other values (2)17348
13.3%
Common
ValueCountFrequency (%)
17348
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII147458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r17348
11.8%
17348
11.8%
i17348
11.8%
l17348
11.8%
P8674
 
5.9%
a8674
 
5.9%
t8674
 
5.9%
s8674
 
5.9%
p8674
 
5.9%
e8674
 
5.9%
Other values (3)26022
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct815
Distinct (%)0.09395895780493428
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.5817473709937745
Minimum0.0
Maximum2.15
Zeros20
Zeros (%)0.002305741295826608
Memory size69520
2022-10-20T19:31:08.403517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.145833
Q10.325
median0.504167
Q30.775
95-th percentile1.266667
Maximum2.15
Range2.15
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.3479908775
Coefficient of variation (CV)0.5981821232
Kurtosis0.9736829925
Mean0.581747371
Median Absolute Deviation (MAD)0.2125
Skewness1.007606956
Sum5046.076696
Variance0.1210976509
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.940737445 × 10-7
2022-10-20T18:53:11.520758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.145833
Q10.325
median0.504167
Q30.775
95-th percentile1.266667
Maximum2.15
Range2.15
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.3479908775
Coefficient of variation (CV)0.5981821232
Kurtosis0.9736829925
Mean0.581747371
Median Absolute Deviation (MAD)0.2125
Skewness1.007606956
Sum5046.076696
Variance0.1210976509
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.940737445 × 10-7
2022-10-20T19:31:08.495347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.43333366
 
0.8%
0.366
 
0.8%
0.33333362
 
0.7%
0.49166760
 
0.7%
0.47558
 
0.7%
0.654
 
0.6%
0.34166754
 
0.6%
0.30416754
 
0.6%
0.35416754
 
0.6%
0.262552
 
0.6%
Other values (805)8094
93.3%
ValueCountFrequency (%)
020
0.2%
0.0041674
 
< 0.1%
0.006252
 
< 0.1%
0.0083334
 
< 0.1%
0.0086962
 
< 0.1%
0.0090912
 
< 0.1%
0.0095242
 
< 0.1%
0.012
 
< 0.1%
0.01254
 
< 0.1%
0.0166676
 
0.1%
ValueCountFrequency (%)
2.152
< 0.1%
2.1208332
< 0.1%
2.0791672
< 0.1%
2.0333332
< 0.1%
2.0086962
< 0.1%
1.9708332
< 0.1%
1.9333332
< 0.1%
1.9083332
< 0.1%
1.9041672
< 0.1%
1.8958332
< 0.1%
2022-10-20T18:53:11.855375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.43333366
 
0.8%
0.366
 
0.8%
0.33333362
 
0.7%
0.49166760
 
0.7%
0.47558
 
0.7%
0.654
 
0.6%
0.34166754
 
0.6%
0.30416754
 
0.6%
0.35416754
 
0.6%
0.262552
 
0.6%
Other values (805)8094
93.3%
ValueCountFrequency (%)
020
0.2%
0.0041674
 
< 0.1%
0.006252
 
< 0.1%
0.0083334
 
< 0.1%
0.0086962
 
< 0.1%
0.0090912
 
< 0.1%
0.0095242
 
< 0.1%
0.012
 
< 0.1%
0.01254
 
< 0.1%
0.0166676
 
0.1%
ValueCountFrequency (%)
2.152
< 0.1%
2.1208332
< 0.1%
2.0791672
< 0.1%
2.0333332
< 0.1%
2.0086962
< 0.1%
1.9708332
< 0.1%
1.9333332
< 0.1%
1.9083332
< 0.1%
1.9041672
< 0.1%
1.8958332
< 0.1%
2022-10-20T19:31:08.763295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.195296288
Minimum0
Maximum5.5
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2022-10-20T18:53:12.139784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.195296288
Minimum0
Maximum5.5
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2022-10-20T19:31:08.919441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.7
median1
Q31.6
95-th percentile2.6
Maximum5.5
Range5.5
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.7237984862
Coefficient of variation (CV)0.6055389727
Kurtosis2.069147858
Mean1.195296288
Median Absolute Deviation (MAD)0.4
Skewness1.205610877
Sum10368
Variance0.5238842486
MonotonicityNot monotonic
2022-10-20T18:53:12.309114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.7
median1
Q31.6
95-th percentile2.6
Maximum5.5
Range5.5
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.7237984862
Coefficient of variation (CV)0.6055389727
Kurtosis2.069147858
Mean1.195296288
Median Absolute Deviation (MAD)0.4
Skewness1.205610877
Sum10368
Variance0.5238842486
MonotonicityNot monotonic
2022-10-20T19:31:09.012539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8626
 
7.2%
0.7594
 
6.8%
0.9592
 
6.8%
0.6582
 
6.7%
1534
 
6.2%
0.5516
 
5.9%
1.1448
 
5.2%
0.4438
 
5.0%
1.2436
 
5.0%
1.3434
 
5.0%
Other values (40)3474
40.1%
ValueCountFrequency (%)
020
 
0.2%
0.168
 
0.8%
0.2168
 
1.9%
0.3288
3.3%
0.4438
5.0%
0.5516
5.9%
0.6582
6.7%
0.7594
6.8%
0.8626
7.2%
0.9592
6.8%
ValueCountFrequency (%)
5.54
< 0.1%
52
 
< 0.1%
4.94
< 0.1%
4.72
 
< 0.1%
4.62
 
< 0.1%
4.52
 
< 0.1%
4.42
 
< 0.1%
4.36
0.1%
4.22
 
< 0.1%
4.12
 
< 0.1%

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.35635232
Minimum0
Maximum23
Zeros2160
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2022-10-20T18:53:12.466116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8626
 
7.2%
0.7594
 
6.8%
0.9592
 
6.8%
0.6582
 
6.7%
1534
 
6.2%
0.5516
 
5.9%
1.1448
 
5.2%
0.4438
 
5.0%
1.2436
 
5.0%
1.3434
 
5.0%
Other values (40)3474
40.1%
ValueCountFrequency (%)
020
 
0.2%
0.168
 
0.8%
0.2168
 
1.9%
0.3288
3.3%
0.4438
5.0%
0.5516
5.9%
0.6582
6.7%
0.7594
6.8%
0.8626
7.2%
0.9592
6.8%
ValueCountFrequency (%)
5.54
< 0.1%
52
 
< 0.1%
4.94
< 0.1%
4.72
 
< 0.1%
4.62
 
< 0.1%
4.52
 
< 0.1%
4.42
 
< 0.1%
4.36
0.1%
4.22
 
< 0.1%
4.12
 
< 0.1%

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.35635232
Minimum0
Maximum23
Zeros2160
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2022-10-20T19:31:09.109294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.5473449
Coefficient of variation (CV)0.84070524
Kurtosis-1.778850878
Mean11.35635232
Median Absolute Deviation (MAD)8
Skewness-0.02212517528
Sum98505
Variance91.15179465
MonotonicityNot monotonic
2022-10-20T18:53:12.608303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.5473449
Coefficient of variation (CV)0.84070524
Kurtosis-1.778850878
Mean11.35635232
Median Absolute Deviation (MAD)8
Skewness-0.02212517528
Sum98505
Variance91.15179465
MonotonicityNot monotonic
2022-10-20T19:31:09.222298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
02160
24.9%
231236
14.2%
7734
 
8.5%
20684
 
7.9%
21680
 
7.8%
19654
 
7.5%
1631
 
7.3%
22554
 
6.4%
8355
 
4.1%
2216
 
2.5%
Other values (14)770
 
8.9%
ValueCountFrequency (%)
02160
24.9%
1631
 
7.3%
2216
 
2.5%
374
 
0.9%
424
 
0.3%
534
 
0.4%
6166
 
1.9%
7734
 
8.5%
8355
 
4.1%
972
 
0.8%
ValueCountFrequency (%)
231236
14.2%
22554
6.4%
21680
7.8%
20684
7.9%
19654
7.5%
18212
 
2.4%
1756
 
0.6%
1610
 
0.1%
156
 
0.1%
146
 
0.1%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct34
Distinct (%)0.007814295564238107
Missing4323
Missing (%)0.49838598109292137
Infinite0
Infinite (%)0.0
Mean11.449551827166168
Minimum0.0
Maximum38.0
Zeros14
Zeros (%)0.0016140189070786258
Memory size69520
2022-10-20T18:53:12.745310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
02160
24.9%
231236
14.2%
7734
 
8.5%
20684
 
7.9%
21680
 
7.8%
19654
 
7.5%
1631
 
7.3%
22554
 
6.4%
8355
 
4.1%
2216
 
2.5%
Other values (14)770
 
8.9%
ValueCountFrequency (%)
02160
24.9%
1631
 
7.3%
2216
 
2.5%
374
 
0.9%
424
 
0.3%
534
 
0.4%
6166
 
1.9%
7734
 
8.5%
8355
 
4.1%
972
 
0.8%
ValueCountFrequency (%)
231236
14.2%
22554
6.4%
21680
7.8%
20684
7.9%
19654
7.5%
18212
 
2.4%
1756
 
0.6%
1610
 
0.1%
156
 
0.1%
146
 
0.1%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct34
Distinct (%)0.007814295564238107
Missing4323
Missing (%)0.49838598109292137
Infinite0
Infinite (%)0.0
Mean11.449551827166168
Minimum0.0
Maximum38.0
Zeros14
Zeros (%)0.0016140189070786258
Memory size69520
2022-10-20T19:31:09.369772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median10
Q315
95-th percentile24
Maximum38
Range38
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.52409639
Coefficient of variation (CV)0.5698123812
Kurtosis0.8707412411
Mean11.44955183
Median Absolute Deviation (MAD)4
Skewness0.959177419
Sum49817
Variance42.56383371
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.760995906 × 10-5
2022-10-20T18:53:12.916718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median10
Q315
95-th percentile24
Maximum38
Range38
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.52409639
Coefficient of variation (CV)0.5698123812
Kurtosis0.8707412411
Mean11.44955183
Median Absolute Deviation (MAD)4
Skewness0.959177419
Sum49817
Variance42.56383371
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.760995906 × 10-5
2022-10-20T19:31:09.478904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7386
 
4.5%
8344
 
4.0%
9342
 
3.9%
10334
 
3.9%
6328
 
3.8%
5296
 
3.4%
11292
 
3.4%
15212
 
2.4%
13210
 
2.4%
14188
 
2.2%
Other values (24)1419
 
16.4%
(Missing)4323
49.8%
ValueCountFrequency (%)
014
 
0.2%
144
 
0.5%
2118
 
1.4%
3186
2.1%
5296
3.4%
6328
3.8%
7386
4.5%
8344
4.0%
9342
3.9%
10334
3.9%
ValueCountFrequency (%)
382
 
< 0.1%
362
 
< 0.1%
3510
 
0.1%
3410
 
0.1%
3324
0.3%
328
 
0.1%
314
 
< 0.1%
3016
0.2%
2810
 
0.1%
2728
0.3%
2022-10-20T18:53:13.275700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7386
 
4.5%
8344
 
4.0%
9342
 
3.9%
10334
 
3.9%
6328
 
3.8%
5296
 
3.4%
11292
 
3.4%
15212
 
2.4%
13210
 
2.4%
14188
 
2.2%
Other values (24)1419
 
16.4%
(Missing)4323
49.8%
ValueCountFrequency (%)
014
 
0.2%
144
 
0.5%
2118
 
1.4%
3186
2.1%
5296
3.4%
6328
3.8%
7386
4.5%
8344
4.0%
9342
3.9%
10334
3.9%
ValueCountFrequency (%)
382
 
< 0.1%
362
 
< 0.1%
3510
 
0.1%
3410
 
0.1%
3324
0.3%
328
 
0.1%
314
 
< 0.1%
3016
0.2%
2810
 
0.1%
2728
0.3%
2022-10-20T19:31:09.633862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T18:52:55.758844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T19:31:00.232354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:52:55.487936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:31:00.077287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:52:55.898449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:31:00.307813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:52:55.634300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:31:00.156915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-20T18:53:13.531194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/2022-10-20T19:31:09.769383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T18:53:13.885418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T19:31:09.893728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T18:53:14.157449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T19:31:10.039231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T18:53:14.438758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T19:31:10.250807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T18:53:14.689476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T19:31:10.528744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T18:53:14.890266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T19:31:10.645748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T18:52:56.208484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T19:31:00.470261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T18:52:56.866654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T19:31:00.808904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T18:52:57.182754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T19:31:00.965865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T18:52:57.359285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T19:31:01.047643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
041330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-01Parts per billion47.208333102.021101Parts per million0.0197500.039933Parts per billion1.7500002.003.0Parts per million0.7875001.922NaN
141330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-01Parts per billion47.208333102.021101Parts per million0.0197500.039933Parts per billion1.7500002.003.0Parts per million0.6210531.32315.0
241330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-01Parts per billion47.208333102.021101Parts per million0.0197500.039933Parts per billion1.7375002.02NaNParts per million0.7875001.922NaN
341330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-01Parts per billion47.208333102.021101Parts per million0.0197500.039933Parts per billion1.7375002.02NaNParts per million0.6210531.32315.0
441330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-02Parts per billion28.08333379.02378Parts per million0.0142500.0271023Parts per billion1.3750002.003.0Parts per million0.4750001.522NaN
541330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-02Parts per billion28.08333379.02378Parts per million0.0142500.0271023Parts per billion1.3750002.003.0Parts per million0.5541671.3015.0
641330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-02Parts per billion28.08333379.02378Parts per million0.0142500.0271023Parts per billion1.3625002.02NaNParts per million0.4750001.522NaN
741330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-02Parts per billion28.08333379.02378Parts per million0.0142500.0271023Parts per billion1.3625002.02NaNParts per million0.5541671.3015.0
841330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-03Parts per billion62.714286170.019114Parts per million0.0162920.0322227Parts per billion1.0333333.320NaNParts per million1.1791671.71119.0
941330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2000-01-03Parts per billion62.714286170.019114Parts per million0.0162920.0322227Parts per billion1.0333333.320NaNParts per million1.2227273.67NaN

Last rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
866441330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-29Parts per billion16.04545528.01326Parts per million0.0179170.0352230Parts per billion1.3478263.094.0Parts per million0.2478260.40NaN
866541330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-29Parts per billion16.04545528.01326Parts per million0.0179170.0352230Parts per billion1.3478263.094.0Parts per million0.3541670.809.0
866641330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-30Parts per billion11.29166735.02233Parts per million0.0245830.037831Parts per billion1.0750001.623NaNParts per million0.2416670.822NaN
866741330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-30Parts per billion11.29166735.02233Parts per million0.0245830.037831Parts per billion1.0750001.623NaNParts per million0.1750000.5236.0
866841330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-30Parts per billion11.29166735.02233Parts per million0.0245830.037831Parts per billion1.0833332.0223.0Parts per million0.2416670.822NaN
866941330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-30Parts per billion11.29166735.02233Parts per million0.0245830.037831Parts per billion1.0833332.0223.0Parts per million0.1750000.5236.0
867041330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-31Parts per billion18.45833326.0025Parts per million0.0180530.0311426Parts per billion1.0833332.073.0Parts per million0.4666670.728.0
867141330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-31Parts per billion18.45833326.0025Parts per million0.0180530.0311426Parts per billion1.0750001.38NaNParts per million0.4041670.87NaN
867241330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-31Parts per billion18.45833326.0025Parts per million0.0180530.0311426Parts per billion1.0833332.073.0Parts per million0.4041670.87NaN
867341330032857 N MILLER RD-S SCOTTSDALE STNArizonaMaricopaScottsdale2010-12-31Parts per billion18.45833326.0025Parts per million0.0180530.0311426Parts per billion1.0750001.38NaNParts per million0.4666670.728.0